Adjuvant treatment following radical cystectomy for muscle-invasive urothelial carcinoma and variant histologies: Is there a role for radiotherapy?
Bibliographic record
Abstract
Comprehensive molecular characterisation of muscle-invasive urothelial carcinoma and variant histological subtypes has led to the identification of recurrent driver mutations that are distinct in these aggressive subgroups of bladder cancer. While distant metastasis dominates as a pattern of relapse following radical cystectomy or chemoradiotherapy, loco-regional control rates are also suboptimal with single modality local treatment, and likewise, harbour equivocal implications on the long-term prognosis of patients. The role of adjuvant radiotherapy for optimising disease control within the pelvis is controversial, with limited evidence to support its efficacy. Herein, we present a stepwise review on adjuvant radiotherapy post-cystectomy; first, discussing the evidence to date supporting the concept that adjuvant radiotherapy is effective in targeting occult metastases within the pelvis, and adds to the benefits of adjuvant chemotherapy. Next, we outlined the principles underlying the definition of radiotherapy target volumes. To conclude, we addressed the need for appropriate patient stratification for treatment intensification, based on existing clinical models and novel molecular indices of aggression in muscle-invasive urothelial cancers and variant histological subtypes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".